Alongside partners, AWS and IBM, GigaSpaces showcased its Enterprise RAG solution this week at IBM Think.

Many organizations are struggling to implement GenAI in a way that offers tangible business value. One reason is trust and accuracy.

Results of a survey shared at the recent Gartner Data and Analytics Summit in London reveal that 46% of respondents cited data accuracy, reliability and transparency as their top challenge when implementing Generative AI.

Most LLMs that have been trained on publicly available information offer reasonable levels of accuracy on generic topics. But when these LLMs query your own proprietary enterprise data, their level of accuracy plunges.

At IBM Think, GigaSpaces eRAG showcased our eRAG solution, which aims to solve this problem by delivering a level of human accuracy when querying structured data in natural language.

Our goal is to empower organizations to rely on GenAI to make trustworthy business decisions when using natural language to query their backend systems – a capability not currently available with generic LLMs.

During the event, IBM and AWS announced the expansion of the watsonx portfolio on AWS. GigaSpaces is excited to be part of this initiative and is integrating with watsonx.governance on AWS to deliver robust AI governance capabilities for creating a trusted foundation for our eRAG solution.

Mr. Elkin explained how GigaSpaces decided to address this challenge with an innovative new approach that provides LLMs with the “missing context” about the nature of the structured data. “When AI models look at your company’s internal records it sees a neatly organized cupboard full of drawers, but it really can’t tell what each drawer is containing or the rationale of sorting the company data in the way it’s currently stored. This is exactly why current LLMs fail to provide accurate responses to queries on structure data. We’re on a mission to change that”.